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Mr Yangyang Shu

Mr Yangyang Shu

Associate Lecturer

Ph.D. in Computer Science, University of Technology Sydney

UNSW Canberra
School of Systems & Computing

Dr Yangyang Shu is an associate lecturer in the School of Systems and Computing at the University of New South Wales, Canberra.

Before joining UNSW, he was a research fellow at the Australian Institute for Machine Learning (AIML), the University of Adelaide and worked on the project - The Centre for Augmented Reasoning (CAR). 

His papers have been published in top-tier conferences and journals in the fields of machine learning and computer vision, including CVPR (2024), CVPR (2023), ECCV (2022), TMM (2022), PR (2022), TMM (2021), etc. More information about him can be found on my homepage: https://ganperf.github.io/yangyangshu.github.io/

 
E-mail
yangyang.shu@unsw.edu.au
Location
Building 15, Room 209
  • Journal articles | 2024
    Shu Y; Li Q; Liu L; Xu G, 2024, 'Semi-Supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment', IEEE Transactions on Multimedia, 26, pp. 4086 - 4096, http://dx.doi.org/10.1109/TMM.2021.3117709
    Journal articles | 2022
    Shu Y; Li Q; Liu L; Xu G, 2022, 'Privileged multi-task learning for attribute-aware aesthetic assessment', Pattern Recognition, 132, http://dx.doi.org/10.1016/j.patcog.2022.108921
    Journal articles | 2022
    Shu Y; Li Q; Xu C; Liu S; Xu G, 2022, 'V-SVR+: Support Vector Regression With Variational Privileged Information', IEEE Transactions on Multimedia, 24, pp. 876 - 889, http://dx.doi.org/10.1109/TMM.2021.3060955
    Journal articles | 2021
    Wang S; Wang C; Chen T; Wang Y; Shu Y; Ji Q, 2021, 'Video Affective Content Analysis by Exploring Domain Knowledge', IEEE Transactions on Affective Computing, 12, pp. 1002 - 1017, http://dx.doi.org/10.1109/taffc.2019.2912377
    Journal articles | 2020
    Shu Y; Li Q; Liu S; Xu G, 2020, 'Learning with privileged information for photo aesthetic assessment', Neurocomputing, 404, pp. 304 - 316, http://dx.doi.org/10.1016/j.neucom.2020.04.142
  • Conference Papers | 2024
    Zhou Z; Xu H-M; Shu Y; Liu L, 2024, 'Unlocking the Potential of Pre-Trained Vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors', in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 3817 - 3827, presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 16 June 2024 - 22 June 2024, http://dx.doi.org/10.1109/cvpr52733.2024.00366
    Conference Papers | 2023
    Shu Y; Van den Hengel A; Liu L, 2023, 'Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems', in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 11392 - 11401, presented at 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 17 June 2023 - 24 June 2023, http://dx.doi.org/10.1109/cvpr52729.2023.01096
    Conference Papers | 2022
    Shu Y; Yu B; Xu H; Liu L, 2022, 'Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-boosting Attention Mechanism', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 449 - 465, http://dx.doi.org/10.1007/978-3-031-19806-9_26
    Conference Papers | 2020
    Shu Y; Sui Y; Zhang H; Xu G, 2020, 'Perf-AL: Performance prediction for configurable software through adversarial learning', in International Symposium on Empirical Software Engineering and Measurement, http://dx.doi.org/10.1145/3382494.3410677
    Conference Papers | 2017
    Shu Y; Wang S, 2017, 'Emotion recognition through integrating EEG and peripheral signals', in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 2871 - 2875, presented at 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 05 March 2017 - 09 March 2017, http://dx.doi.org/10.1109/icassp.2017.7952681

My research interests include:

Low Supervised Learning (Weakly/Semi-/Self Supervised Learning)

Adaptable Machine Learning and Rationale-Guided Machine Learning

Generative AI, Machine Learning in Music

 

My Teaching

ZEIT 2103, Data Structures and Representation, 2025, S1